mpw-005 / MM2 Metal 2

the MM2 shapes of all of mpw-005

40 of 40 dies rendered.

523,692,389 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 (509.2 MB in total, largest 19.6 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_MM2_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_MM2_reticle.png
the stitched reticle, full resolution
256.9 MB 9cf140594f9d29c6f8da38884bec868c92ce453b82421c7f19dd2714ceef146cf4135302 9daf50dc593e9139280c8702ec2ea85f06db83c4c078954efc98716adcd46f03
mpw-005_MM2_reticle_overview.png
the same image scaled to fit 4096 px
5.8 MB 19a2e28caf04902c005da3565dfc0a1ded0df3bfc1f9895b50e5b3cea4a9a062d11dbbdf 70f979c822ded6c04675282e5a1ed14f81b3f71c1eb6b248c4c03e60fc5b5cf0
mpw-005_MM2_tiles.json
where every tile belongs, and its checksum
13.5 kB 1fc8c9a9d731177d572b47d4c931bea4c9e12678be077a8025165771338a5592a4ef5d42 85d5c93fd6fb75f426006a62ae8e72baaefa6cc07223886ffbcf46b71943352f
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 7.1 MB786bc4b9dddf553efd8a233f830b9a257517eef4c0abaa965e8503d0f283b5d7
A2 mpw5_cache 7576×107767576, 0 9.1 MB53e06fc310ab8c17e1941a62b4959ac77d51cfa040f4b51db600b3667f628fb5
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 18.8 MB479e159259c10363f508ed842be33239c328673232a2545823c3b81b6fb149e2
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 17.1 MB512cf85f405e42d8a69381526548eb025e4726d6a759ff1fa9d154840b1a6491
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 17.3 MBa48a9b28208075c0a85a383cf80e7c5eec73e4b2a0a02188b7a5276f0ac95f8b
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 17.6 MB2acfb9b7bf1593fa5f33f48f80393c5479d42078771eecf4522955fe258f34e7
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 13.6 MB5f0468cf10bac4827843452dd55d9740c2736c5a99a68197bafc3a32d3758633
B2 microwatt_mpw5 7576×107767576, 10776 19.6 MB7d9f75bedf5a29bdbe62075f96a64cc1a081b7250a5fa82ae845a3b020774985
B3 armleo_gpio_mpw5 7576×1077615152, 10776 10.2 MB58dc6ae82c7c1f379239bd19f28820e98bfc1eb5aef08a6058eb2bd19da5dc0c
B4 fabulous_efpga 7576×1077622728, 10776 18.2 MB6874374a8166c50221eb30ccc7b2431298791186fbb783d7bbf4150d5f635db8
B5 i2c_controller 7576×1077630304, 10776 12.6 MBf038476354630696eb8be980f3782f99a7010a4b88f5bc1e668e21592fbe77e5
B6 lbist-mbist 7576×1077637880, 10776 14.7 MB6ca598ea790b3d4dd0c5bb7e6c3f3a964c7e9fd2ec7995133accef75c9df3acf
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 16.5 MB35801676680255de99393dbcf74359728c0ea2192855f8302cdd0c94a9caa327
C1 riscduino-qcore__q0_ 7576×107760, 21552 16.5 MB022e05041491d7ec959394a01cbd3260fd0871a299588e469c12a51a636fd912
C2 high_speed_vco 7576×107767576, 21552 7.1 MB3dc4d47416477e4cfe7592a282c365628ef19257a2cf7ac51931a0d2ef27e3a7
C3 openfasoc-cryo-gen 7576×1077615152, 21552 9.9 MBe275a6993d4ffa86811653e406c6b31d3ef0d8b95a11c9a5e465526b144a7890
C4 microwave_signal_generator 7576×1077622728, 21552 7.7 MBcf9b956a48443c93f94aa9f2f1ee48da573aa8492ee3d09ebb8e937f09f37cf7
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 13.5 MB5c3bb509dfb38a647e610ef0c63fa20e8d5ad75d780084c9ab0988e8f6c4e2f7
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 7.2 MB070201644e6f97f2accff7830f2a0d2223a8a519723f4e573a78e325238d2ede
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 7.1 MBc55c5b12addd36d0c1fb9daae71acdbc360f860960e2af45256251976ef09dce
D2 azadi_iii 7576×107767576, 32328 13.4 MB2310e9412400581582f1ff927d116752c32faa1bcfbecce8d15acc4c606a3d04
D3 tia_for_physic_experiments 7576×1077615152, 32328 7.2 MB084a200204dce445df5ef9bc817f0a5973bf665bc5585619baad63935cb3b3b4
D4 qf105 7576×1077622728, 32328 17.7 MB6803b8fb7c3ec6759acb504579300c7e6911ab5c5905efdee7767ee287e6dabf
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 16.5 MBb461dc05625c1121642d1962829c29c68da31758c6322fac6d8b2aeb0bc96f51
D6 junga_soc_mpw5 7576×1077637880, 32328 13.8 MBad2a659c6e208fcf3d846aa9a5988db3b52ff397f27691e22bde93f3ea83d573
D7 nand_flash_mpw-5 7976×1077645456, 32328 8.8 MB44e01ccb0e314987e2a1e1bd0ee85f95547429b99632e44a25307c3a4949a30e
E1 kasirga-c0-mpw-5 7576×107760, 43104 12.0 MB5498d59b556c95044a1771a2be5e85581e73d17ed517001df6cfb9e411e7dadd
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 13.5 MBbc42dbdf38ab31d53c1437fd56b9febb8334141e47ff24be71b4b4b416d7ce8a
E3 wirelessenergyharvesting 7576×1077615152, 43104 7.2 MB76084f58ca6f00030bfe8b6dbf6c22d6a8c357804531a47f2016376426a4ece2
E4 caravel_raster_engine 7576×1077622728, 43104 16.5 MBff1c5606ba12508809486d66071e736f8db4aae3c6158b0aa811e425305127bd
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 7.2 MB3d953c407e8bb71284c849b49636766bd0208a95d120dd1ec0dec9cb79dd23e2
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 12.1 MB8fecea2762a0dc7dd0e4342f36f7df8b823a66acea694f02d878fe07c4078310
E7 alu 7976×1077645456, 43104 12.3 MB95aaacb7d0d5e324e71c1b591ded8f2dcad73c207027285fa568571a6b1e4317
F1 4ft4 7576×111760, 53880 13.1 MBe8cb206c2ff703b80d1de5f2036767d4ba52a3e9530e019403eb2ab12d82ca21
F2 rng_chaos 7576×111767576, 53880 14.6 MBcbc6a2970a89574dc77f3d0ec626cd4fa6d3a5907a196c98996d9bfd171368df
F3 uetrv-ecore 7576×1117615152, 53880 13.9 MBc7234814acdf5f3cc48a9d7b5233b9df22bf7203a5691a5bbb08cc7ebde05052
F4 systolic_array 7576×1117622728, 53880 13.3 MBdd2e69978df44bf0a0328a29c5c76d5f0d89261794967f526bec1fb4705c07de
F5 reram_test 7576×1117630304, 53880 7.2 MB49918aa74813f6d859cd41400e98d3f9f3f6ff6055c3e1560f9636bfa182be53
F6 secure_memory 7576×1117637880, 53880 14.4 MBb49c5a15b906dccebca5cde9dcd149fed34ce266ed4770dc66f1d5f0e7b0995f
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 12.7 MB2b24aa370d786f0f2acfd5efbbf8fb26e460c61163b6d0e0b20aa5e967f87fdb

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 MM2 on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask MM2

Mask layer
MM2 — Metal 2
Process step
124 — Metal mask2
Drawn layer
69:20
Layer on the mask-layer GDS
41:0
Fill layer
41:28
Also
Metal 2
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.