mpw-005 / P1M Poly 1

the P1M shapes of all of mpw-005

40 of 40 dies rendered.

213,238,980 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 (314.2 MB in total, largest 12.3 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_P1M_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_P1M_reticle.png
the stitched reticle, full resolution
139.9 MB 5a9baa846943d5cf36869e2069445542257b1fdb8208e60b8d2ed135d4c66f2605e19e2a ab2734044e97818ed851e9b7be56dbddf2727f0da76eb79f5a801230c37196d3
mpw-005_P1M_reticle_overview.png
the same image scaled to fit 4096 px
7.6 MB 181ce5e459f7bc953061b1e374d77809ed4ac1ed91a097b76bf5c08e0ec301668bfd1923 2cb69183f5ca6083af213c2b3e443e9de38847c4604de89b54709681040cf082
mpw-005_P1M_tiles.json
where every tile belongs, and its checksum
13.5 kB bdb4696cb9cce0bbeaa019e19ca4399066a44af7aef995801f334cb2de342e39c453d375 7da6e0d0c284626f05d0c72980c68fa9b6be86dc7c326ea055c065f250394712
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 6.5 MBeb9dd1bccfc9c714702c4284f4f6f33ff012ed39132c1f179e34204bf1e743a9
A2 mpw5_cache 7576×107767576, 0 6.8 MB330cd1b84384bd1abdf0fb2a55ceafcf68648871cd1ba1a2a88629cc943c8a59
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 9.1 MB0ff91493ad5a6565c1d5508b1bd72723e8025f4b59250702b95ca97e68a1dde9
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 7.8 MBbedd7f121df9bc30f00f0f9684af2c6c5d32580de42e8e021a29a7d2b9f7ee9f
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 7.6 MBa4282d10492d9e7bdd2de7662b8738bf82ca2e8b9c804dad6f2945897fa21c60
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 8.8 MB13a61f5417dee7326dee15fe08ae50f2d43a2d490c77f5031dcc06f8f9e97210
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 7.0 MB26af696e14c43235dd01f0de4a4a72ce2b6a13948a8cc2cb86f7250ea4d1595a
B2 microwatt_mpw5 7576×107767576, 10776 12.3 MBb38d26ba6bc7e5790c58359aca33b82492aef73629528eda54f2c7d924e798b6
B3 armleo_gpio_mpw5 7576×1077615152, 10776 6.9 MBf1be8187b087a70c92d502af887e4b912b64ad5a229378509638cd4730515ec3
B4 fabulous_efpga 7576×1077622728, 10776 11.1 MB35244d8795f9af55f22987ea02a0e32477c8c0921e6c7c0073aa784f4d37c954
B5 i2c_controller 7576×1077630304, 10776 6.7 MB7e95f6f7b4c65a0199eb9aad1c1045e8af4a47c42049903461ee569ae43f9179
B6 lbist-mbist 7576×1077637880, 10776 8.7 MB82779fb63634946167f8ceff3b9adffcad004ec500b3fb2fc3de7e38e85e09db
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 10.5 MB9e36ede02d247fb62a87c6958be933b62dbd249ec62e78307b68e53733cb3974
C1 riscduino-qcore__q0_ 7576×107760, 21552 10.5 MBa78311a431c4e264e1fce06298004a36c549d2a867a0b0fe5f151963c881094c
C2 high_speed_vco 7576×107767576, 21552 6.5 MB755701554292e5edfef0c27530636cf6d0a03b3782cafc28a5d288a245ef6d63
C3 openfasoc-cryo-gen 7576×1077615152, 21552 7.0 MB63049b297fc57092bfe13c147fd97880663752b58f7ae433c225be8397e23b41
C4 microwave_signal_generator 7576×1077622728, 21552 6.9 MB34898367694d3242331438c64af8bc2a96b620662ae9cab0d8970a1e82d60b4d
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 8.6 MB657d5a67fd5fd524bd1c81fcadc87a93ebb79a205d939ef602cb3ae142fed16b
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 6.6 MB423dbbc0b8a6553ffc85510930f5218c668e2298a9672c460b800e0f18f98395
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 6.5 MB0880e357bd9798cb157fa05e9c12bb7c520d45823aa9db81a5e54f0b52e03246
D2 azadi_iii 7576×107767576, 32328 9.3 MB596c691c7f94eb45e9452ba9af10c8d71f943294510deb8fb34864952371b4a1
D3 tia_for_physic_experiments 7576×1077615152, 32328 6.6 MB4af6fb4b661d7bbac609ebec178ccb3cd4aa91ab7e8c69050e9564363838b6d6
D4 qf105 7576×1077622728, 32328 8.6 MBcaa56213d83e059da0b503e9b4227e3856c6e2092dec022b0976a6da76a85211
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 7.6 MBc3f879fac13ddb4d0e92c993551c2374eea885009d071ffd9e30466924dcd12e
D6 junga_soc_mpw5 7576×1077637880, 32328 8.0 MB3031551876ac6817ea65515fd0b67961907707bed0516bd878641258ac3c0a4a
D7 nand_flash_mpw-5 7976×1077645456, 32328 6.6 MB5e9bb4ef981791d5e34f706f077291b4dacae43ba92b58e9f5828811bbd947cd
E1 kasirga-c0-mpw-5 7576×107760, 43104 8.2 MB0475a244de6fc023524d49a19fcbf86ea476613daf628664ba5ef1fe8dd469c6
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 9.3 MBc791b5d0b0c3442e71aebba5c04f6898c06936e4f68b8ec3b9face061065e9b4
E3 wirelessenergyharvesting 7576×1077615152, 43104 6.5 MB7e10640ed222f7104cfa2a23cad7eb3d4e0a448a6a68caff83c339d52493b148
E4 caravel_raster_engine 7576×1077622728, 43104 10.5 MB3892c86dee01006e731d11a9056c00e6aec543769db44d54fc0605a31e0fa96a
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 6.6 MB29b76689f861ba04f22d19efd2f5385598c40ddcec23cc34ed322d3c19a9c146
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 7.0 MBa1242d7c5de60a3bce7d169fe3bc81571879f9a6ed28f0e8ad9220fefd61da2d
E7 alu 7976×1077645456, 43104 6.8 MB8165cdd3b880bae0eb9b53f2ea0d9fb0bce4d8b642f6253c110944205e929aae
F1 4ft4 7576×111760, 53880 7.5 MB59e1c971312ec245d785e2cd87938c7e5d74dcb817fc9e2be263590c7bb660bb
F2 rng_chaos 7576×111767576, 53880 6.7 MBb0900b18ad0244332db8f413d11a8b6e44dec64e2a9768faee88f46243034fc9
F3 uetrv-ecore 7576×1117615152, 53880 8.2 MB423cd1a575922879c41b9de95cdea37cf199d9993ea3a6d19e7dbcda35074bd4
F4 systolic_array 7576×1117622728, 53880 7.0 MB5e682eb9dca4ef9f82dd585962f0daf8ae55b87e6cdcf8a1ac44fd6c7a0223ad
F5 reram_test 7576×1117630304, 53880 6.5 MB454b85c58c3248449159b167a65b5436bbf739f75f01d03992a0764fab46105b
F6 secure_memory 7576×1117637880, 53880 7.0 MB042c082a092545c9aaa27c1a89b47a1d637a49c4d7297639268f611b58180cec
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 6.9 MBfa4d76267589b908e24015ef09532fa3c1c19736b937e686985718fd53431702

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 P1M on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask P1M

Mask layer
P1M — Poly 1
Process step
61 — Poly mask
Drawn layer
66:20
Layer on the mask-layer GDS
28:0
Fill layer
28:28
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.