mpw-005 / LVTNM Low Vt Nch*

the LVTNM shapes of all of mpw-005

40 of 40 dies rendered.

132,148 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (7.0 MB in total, largest 253.0 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_LVTNM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_LVTNM_reticle.png
the stitched reticle, full resolution
5.0 MB 98ce66c649905887209c8cafdf039e09b9e688a36be027424415fff55eb1c12f6028687e 71144592ca82ffbfdc7217d8dd5b4ce8dc985ea349007ffeed8baf48cf7767d4
mpw-005_LVTNM_reticle_overview.png
the same image scaled to fit 4096 px
384.9 kB ac5378f530117ad9812a5fdba774067636d70bba1fcec9f718608ff00f0a4169d3deb06a ea4e697814af13a9b0a9e6cdcb835a31687772f876494c5b024e832c6ab7cd51
mpw-005_LVTNM_tiles.json
where every tile belongs, and its checksum
13.6 kB 9967577b40c46078ed474a97d2148001c2005c9b0abd4fea7c166759cbe07b8df623c5cc 12aa47a02c1a1095ab1c52978251e919c150c59634cfa9e7391474c706990635
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 152.5 kB3746a75bcca7ae3cca010ecafb5ddde584b3326d981fc0f8676547bb9b757a0a
A2 mpw5_cache 7576×107767576, 0 160.1 kBdfc2a52b5ef04e2bb73314068f4724b33e1e82c9e1f7431e39621072e12a2cc0
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 177.0 kB5469ba37c097af881f1c0da3101970470c9a93e47ff8739c504474de6292b289
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 168.5 kBba3a2d382d4b41db8ca7218e48ded27710ac4a263a5cadc2c3d2169b4c718ba4
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 170.6 kBb6b021bde273cbd23e8a38a144ec54ea3c5c8c306fac4efd891320dee9ed560b
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 172.7 kB7ef2fae5c0cf20a87e729a7a9c774b64fe945ed0b072c8f19287a791373242db
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 161.2 kBd27bc741d256b11cabc4dfdb9f246db8922d462c830b0195d6438bf843742d9a
B2 microwatt_mpw5 7576×107767576, 10776 194.3 kBd76159bc44b118012f695a91c04f70936bd2c616262f4c4c49039f005b6b0dfb
B3 armleo_gpio_mpw5 7576×1077615152, 10776 171.1 kBa7f110c63e0371fcff62e48cd362eecfa68d29faaaaa419a1062f5b276da10a5
B4 fabulous_efpga 7576×1077622728, 10776 253.0 kB61de17b647750096a70a5ed4e5ac5b95d131a6d2a493f70b300c889498f2f726
B5 i2c_controller 7576×1077630304, 10776 160.1 kBdfc2a52b5ef04e2bb73314068f4724b33e1e82c9e1f7431e39621072e12a2cc0
B6 lbist-mbist 7576×1077637880, 10776 179.9 kB49636a50b0adfa1751a9a9815e1a65aa527f844a0f5aea551a8dc88831cc1a48
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 184.3 kBf7ba91a1964a84817889532477c5a25833bd29f962e4b83e241a8419f0f404f4
C1 riscduino-qcore__q0_ 7576×107760, 21552 180.4 kBcc58b89a3fa5c38b74b1ac19a60e18f18cd424b7f6469a7e661302576a2eba54
C2 high_speed_vco 7576×107767576, 21552 152.6 kBdbc72a8e751331220364ec3438fd8423ec518a6dff8e263e9bbfac4a6124a0d5
C3 openfasoc-cryo-gen 7576×1077615152, 21552 181.0 kBf90df8c96898a1726fe9b89a10837b94769d1c659a936eb03dc162612a733d6b
C4 microwave_signal_generator 7576×1077622728, 21552 152.5 kB3746a75bcca7ae3cca010ecafb5ddde584b3326d981fc0f8676547bb9b757a0a
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 167.1 kB2ad974d0d9b6c041d5e1a4768c294afd3f6728d9f20c4b6ca6b0a036658e5559
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 156.6 kBed00cf8045fcc0c0e25d6a2efce9ac6e9c9a9a543c40645d1b29e394f248d81f
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 152.5 kB3746a75bcca7ae3cca010ecafb5ddde584b3326d981fc0f8676547bb9b757a0a
D2 azadi_iii 7576×107767576, 32328 212.8 kBe7da75f27f2a1c73cb6c34bc21f573c4076b4836b49c455c0a87d9ab75e8509d
D3 tia_for_physic_experiments 7576×1077615152, 32328 154.5 kB7194390c3b5ccf405e77c2faeb2569e2d719852d93a981806748761992401ef0
D4 qf105 7576×1077622728, 32328 191.5 kBfa41fc31f2ea32142a4586f6ee6153cbef773e159a44669f7a775cb9d52649cd
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 157.6 kB333bb02986e4bb767cf3f90a050afae79c7776ae0172aae5dc4527db2c8c1cba
D6 junga_soc_mpw5 7576×1077637880, 32328 162.3 kBa6ed3d5da8e7e3c67344119d596e3be92a2a98640d0a8b822f8dc636a2f2ec4f
D7 nand_flash_mpw-5 7976×1077645456, 32328 161.6 kB129e252257fc592dcddf070b292a45e8ea900404952cebadd0d62aa25b42af84
E1 kasirga-c0-mpw-5 7576×107760, 43104 166.4 kBda88120009395a522634c088a7f563b4b22bd522a086510ee2e167aa6d11ea4b
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 166.2 kB901af73ebd2539a6cdd0391144f9067e911eb85a24ead21b28047440990a8907
E3 wirelessenergyharvesting 7576×1077615152, 43104 152.5 kB3746a75bcca7ae3cca010ecafb5ddde584b3326d981fc0f8676547bb9b757a0a
E4 caravel_raster_engine 7576×1077622728, 43104 163.2 kB67aeaef54110d261d0897fe959e8cb7528ec6069d22505c903d230c72d3eb050
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 157.0 kB0cbb70b7890c05c7cd91f483ac591624be42639930f56114652d0acd29af6c75
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 163.1 kB5764c4e492f35b146794f09bd343feb9f3b550bca5d91ed135b8abc617a2e14c
E7 alu 7976×1077645456, 43104 163.9 kB5cbba452cb441ddf7776ece8afc6363f4e66e3cd23e2dc148be7bf7d9dff58a3
F1 4ft4 7576×111760, 53880 164.7 kBc1393cf937b1be3d9e6b1ddc6a14597ddfe370c109e517e7a95240ac1e30fd93
F2 rng_chaos 7576×111767576, 53880 162.6 kB793716431f37ee4b986817848341ebfc958ad0cdf40736ae82c1a26201329698
F3 uetrv-ecore 7576×1117615152, 53880 174.5 kB507331c79989998fe4bad1e33cdef02719a79322b4cb5d3437659e4b5eb9146b
F4 systolic_array 7576×1117622728, 53880 164.7 kB4b102e51c0c9f2e07013e1add8ba61b3f461dd05d1a2e44446c086dc37342c53
F5 reram_test 7576×1117630304, 53880 156.4 kB4b60cb656f3c785e1c2217c310449a99788fb2815620705c44f437fd81306c3f
F6 secure_memory 7576×1117637880, 53880 168.9 kBa688c590090fd381c597c1129e1745d4cc83506e7d1c61cc22a3d8e0f61c9ca4
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 168.0 kBfc2eca19c59552bf8d8d9c00dabd0d7ea228b45dca17824192208b16bd953d9e

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About LVTNM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask LVTNM

Mask layer
LVTNM — Low Vt Nch*
Process step
14 — Low Vt NOMOS mask
Drawn layer
125:44
Derived from drawn layers as
125:44 OR (64:20 AND (78:44 OR 81:2))
Layer on the mask-layer GDS
25:0
Detail
drawn lvtn plus a created part [nwell AND (hvtp OR areaid.ce)] OR (LV nwell over varactors); only the drawn part is rendered
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.