mpw-005 / MM3 Metal 3-PLM

the MM3 shapes of all of mpw-005

40 of 40 dies rendered.

198,625,512 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 (160.8 MB in total, largest 7.7 MB), 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_MM3_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_MM3_reticle.png
the stitched reticle, full resolution
75.6 MB 7bb9207f6a4dce876d63f511f355f8b484d1be7b57c870e43a587a2c65a4bb1d7d4c7072 37b59163cec31e7a52052a4c2b1e771e35e8bfff565354176175860ad51f3843
mpw-005_MM3_reticle_overview.png
the same image scaled to fit 4096 px
4.8 MB 243942988db3cb9267c4e198ddba461639dd0f992d3020590df281073ff1a152a21a05bf 5156bf3a3774a1a30e60aec8d2aa8e3959524e44098f4bbf86197e816f07ce9b
mpw-005_MM3_tiles.json
where every tile belongs, and its checksum
13.5 kB d207471618f3fee8de375714692e9176a9a156595807650d2a5ea17377cd9366cb5cd78d 9b84d7a93d6737e27b256b4d40758642f1a34beac96c507feef6214c66421072
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 2.9 MB4aac09834591124564edce52815a23f02e97b9048e61db9bb2a4586e5de67074
A2 mpw5_cache 7576×107767576, 0 3.2 MBf1c825ad96dbeba2bb3950b8c34d05144edd4df38fb0a8eceb9f92c70f077526
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 4.7 MBe4a9781c032cc5f41e4ecb917ccbad1330a8b9ef18a9bc14148d113dd3e61cae
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 4.2 MBba367c4b4e4959f8aaf85febd5878837a12e84eb581cf4425b979a0035febd4f
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 4.2 MB2f5f0c23b9ebd067e3cff0e8f6e9e6192d0b17ae73ebb1d26527ec282eeef820
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 4.6 MBcd4b1e18e5053324a55ae93404b53222bcdd04dd5fca537aae4bd0be3b02a407
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 3.3 MB2c11da17242e331c0b5dfdc738d53ad66b2fd17cb5211855716c7ad4cb969095
B2 microwatt_mpw5 7576×107767576, 10776 6.9 MBe7795a4ebfa2edce2a7db34e75b79ab0c2582af910e35e61f75e995522a2cc81
B3 armleo_gpio_mpw5 7576×1077615152, 10776 4.7 MB724821e6e4c70fb0ccd9e242bda11a03a9e4d8b49a8cdb6f35966ac4485243a2
B4 fabulous_efpga 7576×1077622728, 10776 7.7 MB06febc1128e27d612cb88e89bc22548e6239c3a33e282e6169c95a2692c8c1c5
B5 i2c_controller 7576×1077630304, 10776 3.2 MBcf43382178388dbd5a5b78178711e38a971332155b42b7111bf07b24c11a3acd
B6 lbist-mbist 7576×1077637880, 10776 4.8 MBfd13a5f4146578bb990302729efbbcfb51a02203356d40d59e79709e453e3403
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 5.1 MBe96ba299ac1d345c3dd7eedd54fd11d096fc26e5f04d41787a4cb34db9801436
C1 riscduino-qcore__q0_ 7576×107760, 21552 5.1 MBc34de3ae1cd449b94162db62535718e1b715d270e092b23a3f609e4bb5d168a1
C2 high_speed_vco 7576×107767576, 21552 3.0 MB1f479c187d4054977be9389ad0eacc0dabf4fbd3bb02cad5233896c3bbb15ed6
C3 openfasoc-cryo-gen 7576×1077615152, 21552 5.1 MBb8850d5f039ca31942485c2812d94a03b68918b3562968a194c714cc6df596d5
C4 microwave_signal_generator 7576×1077622728, 21552 3.4 MB4bf6649a784624856523ce17177788da8834f3047714880fc5fa8314fad1309e
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 3.8 MB76707fa0373f440a29410f9108a3aca6e24e1c401e90bb7feff405dded9ccbc1
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 2.9 MBcf050c4e03b2cd0538d5ebce29403425f89744cb17227b3ed1e9da748d657cc6
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 2.9 MB767e4d75d72b1a2c5001bd2a18d7a9fb9ddbd62def467cff6c28c48ad1747219
D2 azadi_iii 7576×107767576, 32328 5.1 MB077c39d0124bad7436cc3d4298dfdc973ad023bdc58c018eb8ae03c6a15d57ac
D3 tia_for_physic_experiments 7576×1077615152, 32328 3.0 MB7be4f56dad699a9a6c7b7211f37ec8d7262af2c56139001a28c6006c91cd9bf4
D4 qf105 7576×1077622728, 32328 5.8 MBf03d5dd7123d49c7d12d6498e0475aafbca87fbdada6287bed330bed676dc9ee
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 4.9 MB6c23605dbe73188c8b80789e6362e7ad68846d61e2ee891b933ae03ac4bdf5ba
D6 junga_soc_mpw5 7576×1077637880, 32328 3.7 MBc8925a3746ca0ba7bea0bdf4d9d21fcdfa85cf2bc387c5fc0f3e05f3de7b277a
D7 nand_flash_mpw-5 7976×1077645456, 32328 3.1 MBe374111bec4bfdffe23fd99c8f0f229338179ac4f9696733d5c3e450975c2484
E1 kasirga-c0-mpw-5 7576×107760, 43104 4.1 MB8d42fc902e795a1f240d24b99968c35f367c7ed13a77962b062baa885c43ced4
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 4.3 MB6e89af8593cf32d61648add5656ae56ea2c386b711394c6a5b6325951db3ceba
E3 wirelessenergyharvesting 7576×1077615152, 43104 3.2 MB722afc5fbd293e892000528c44a20c7f66dd2bcc647b9f2d5720c128c2c57f50
E4 caravel_raster_engine 7576×1077622728, 43104 4.1 MB414b882cdb76b9d3d22928a0133909738cb3e229052b843cd18464b2cf66ffbe
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 3.0 MB526818688a6b0adf2425cd56f4a3e69ae780cbcefab37ad6bd56a0d95bef6d04
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 3.5 MB93cdc709c4b738cca004704bd6eea6e7bc50304a542581c29470513f7c9ef5c2
E7 alu 7976×1077645456, 43104 3.3 MB5529769b83fd5e55bf3d4eb636b707b5c0ff7460b25db375e0cf266982a6fa48
F1 4ft4 7576×111760, 53880 3.4 MB7ed58cc3c9811b5354285278b4049eea6771da45def8bb35c0a60a9895923af7
F2 rng_chaos 7576×111767576, 53880 3.2 MB293d70c9189310b018fb89303272bd2ef48ed2a9ac0d27098de667466eb22092
F3 uetrv-ecore 7576×1117615152, 53880 4.2 MB29ed21265363c2c85787824ba88a966af85fc2d29930fa13a9e7e9fa15e5ac8d
F4 systolic_array 7576×1117622728, 53880 3.3 MBf3d61aca2a7c2d31f55fdada4f5b6feddc872724d5e99384ac3587207ffde7fe
F5 reram_test 7576×1117630304, 53880 2.9 MB515f6c28426600b6cb2f92e6102bf7f9a6c734b0e3641d7c36d499a25d14d4ea
F6 secure_memory 7576×1117637880, 53880 3.6 MBa4157241d0ac716958eb5c9eb0be0d9dcf3a138513da97255831c39c3fdae8fa
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 3.3 MB38cb2458d8fddad4fb51bebf65521817e16922e687de6854533355b6be25486b

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 MM3 on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask MM3

Mask layer
MM3 — Metal 3-PLM
Process step
139 — Metal3 mask
Drawn layer
70:20
Layer on the mask-layer GDS
34:0
Fill layer
34:28
Variants
Metal 3-TLM, Metal 3-S8TM, Metal 3-PLM
Also
Metal 3
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.